File size: 81,237 Bytes
6fa4bc9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 |
{
"paper_id": "2020",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T01:37:20.320831Z"
},
"title": "BENTO: A Visual Platform for Building Clinical NLP Pipelines Based on CodaLab",
"authors": [
{
"first": "Yonghao",
"middle": [],
"last": "Jin",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "University of Massachusetts Lowell",
"location": {
"region": "MA",
"country": "USA"
}
},
"email": ""
},
{
"first": "Fei",
"middle": [],
"last": "Li",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "University of Massachusetts Lowell",
"location": {
"region": "MA",
"country": "USA"
}
},
"email": ""
},
{
"first": "Hong",
"middle": [],
"last": "Yu",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "University of Massachusetts Lowell",
"location": {
"region": "MA",
"country": "USA"
}
},
"email": ""
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "CodaLab 1 is an open-source web-based platform for collaborative computational research. Although CodaLab has gained popularity in the research community, its interface has limited support for creating reusable tools that can be easily applied to new datasets and composed into pipelines. In clinical domain, natural language processing (NLP) on medical notes generally involves multiple steps, like tokenization, named entity recognition, etc. Since these steps require different tools which are usually scattered in different publications, it is not easy for researchers to use them to process their own datasets. In this paper, we present BENTO, a workflow management platform with a graphic user interface (GUI) that is built on top of CodaLab, to facilitate the process of building clinical NLP pipelines. BENTO comes with a number of clinical NLP tools that have been pre-trained using medical notes and expert annotations and can be readily used for various clinical NLP tasks. It also allows researchers and developers to create their custom tools (e.g., pretrained NLP models) and use them in a controlled and reproducible way. In addition, the GUI interface enables researchers with limited computer background to compose tools into NLP pipelines and then apply the pipelines on their own datasets in a \"what you see is what you get\" (WYSIWYG) way. Although BENTO is designed for clinical NLP applications, the underlying architecture is flexible to be tailored to any other domains.",
"pdf_parse": {
"paper_id": "2020",
"_pdf_hash": "",
"abstract": [
{
"text": "CodaLab 1 is an open-source web-based platform for collaborative computational research. Although CodaLab has gained popularity in the research community, its interface has limited support for creating reusable tools that can be easily applied to new datasets and composed into pipelines. In clinical domain, natural language processing (NLP) on medical notes generally involves multiple steps, like tokenization, named entity recognition, etc. Since these steps require different tools which are usually scattered in different publications, it is not easy for researchers to use them to process their own datasets. In this paper, we present BENTO, a workflow management platform with a graphic user interface (GUI) that is built on top of CodaLab, to facilitate the process of building clinical NLP pipelines. BENTO comes with a number of clinical NLP tools that have been pre-trained using medical notes and expert annotations and can be readily used for various clinical NLP tasks. It also allows researchers and developers to create their custom tools (e.g., pretrained NLP models) and use them in a controlled and reproducible way. In addition, the GUI interface enables researchers with limited computer background to compose tools into NLP pipelines and then apply the pipelines on their own datasets in a \"what you see is what you get\" (WYSIWYG) way. Although BENTO is designed for clinical NLP applications, the underlying architecture is flexible to be tailored to any other domains.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Abstract",
"sec_num": null
}
],
"body_text": [
{
"text": "With the machine learning research going deep, computational models are becoming increasingly large with intensive hyper-parameters tuning, making the research difficult to reproduce. To tackle 1 codalab.org Figure 1 : The architecture of BENTO. The BENTO back end stores the description files of various tools (e.g., pre-trained NLP models), processes static contents of the application and handles compilation of the user-defined pipelines. The CodaLab back end stores the datasets (bundles) and executes computational jobs. The two back end servers are brought behind a single domain name using a reverse proxy server. this problem, researchers have developed CodaLab as an open-source platform for researchers and software developers. However, CodaLab has limited support for reusable tools that can be easily applied to different datasets and be composed into computational pipelines.",
"cite_spans": [],
"ref_spans": [
{
"start": 208,
"end": 216,
"text": "Figure 1",
"ref_id": null
}
],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Building pipelines is essential for the research of certain domains. Take the medical informatics research as an example, a complete NLP analysis on medical notes often involves multiple steps like tokenization, de-identification (Dernoncourt et al., 2017; , entity recognition (Li et al., 2018; Jagannatha and Yu, 2016) and normalization (Li et al., , 2017 Cho et al., 2017) , relation extraction (Li et al., 2018; He et al., 2019) , etc. Since these steps require different tools and these tools are usually scattered in different publications, it is far from trivial to leverage these tools on new datasets even though the authors have released the source code. Therefore, we developed a user-friendly workflow management platform, BiomEdical Nlp TOolkits (BENTO), to facilitate the process of building and applying of clinical NLP pipelines.",
"cite_spans": [
{
"start": 230,
"end": 256,
"text": "(Dernoncourt et al., 2017;",
"ref_id": "BIBREF4"
},
{
"start": 278,
"end": 295,
"text": "(Li et al., 2018;",
"ref_id": "BIBREF13"
},
{
"start": 296,
"end": 320,
"text": "Jagannatha and Yu, 2016)",
"ref_id": "BIBREF9"
},
{
"start": 339,
"end": 357,
"text": "(Li et al., , 2017",
"ref_id": "BIBREF21"
},
{
"start": 358,
"end": 375,
"text": "Cho et al., 2017)",
"ref_id": "BIBREF2"
},
{
"start": 398,
"end": 415,
"text": "(Li et al., 2018;",
"ref_id": "BIBREF13"
},
{
"start": 416,
"end": 432,
"text": "He et al., 2019)",
"ref_id": "BIBREF8"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "The architecture of BENTO is illustrated in Figure 1 . BENTO has three main components. The web interface is supported by two back ends brought together by a reverse-proxy server. The CodaLab back end stores the datasets and executes computational jobs. The BENTO back end serves tool information and transforms user-defined pipelines to CodaLab commands.",
"cite_spans": [],
"ref_spans": [
{
"start": 44,
"end": 52,
"text": "Figure 1",
"ref_id": null
}
],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "The advantages of such architecture are two-fold. First, it is flexible to use CodaLab as the back end for adding custom tools (e.g., pre-trained NLP models) and processing data in a controlled and reproducible way. All the tools are containerized with Docker 2 , which makes the platform to keep a unified interface to manage the models and not need to maintain different operating environment for different models. Second, the web interface makes it easier for users to construct NLP pipelines through editing flowcharts and then apply the pipelines to their data. The web-based architecture also makes the platform widely accessible without complex installation and configuration.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "In this paper, we also show the examples of using BENTO to integrate several clinical NLP applications such as hypoglycemia detection and adverse drug event extraction (Li et al., 2018) , and build pipelines based on these tools. BENTO helps build NLP pipelines, which is a promising system to accelerate the medical informatics research.",
"cite_spans": [
{
"start": 168,
"end": 185,
"text": "(Li et al., 2018)",
"ref_id": "BIBREF13"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Galaxy (Afgan et al., 2018 ) is a similar computational platform that is focused in bioinformatics and computational biology, whose interface inspires the design of ours. The main restriction of the Galaxy platform is that users can only access the tools managed by administrators and cannot define their own tools. In linguistic research community, other related platforms include lingvis.io (El-Assady et al., 2019) , which is focused on integrating NLP operations with visualizations , and Argo (Rak et al., 2012) , a web-based text mining workbench based on the UIMA framework. Stanford CoreNLP (Manning et al., 2014) provides a commonly used NLP tool set. On the library level, NLTK (Hardeniya et al., 2016 ) is a popular Python library that inte-grates multiple widely used NLP tools. OpenNLP (Morton et al., 2005 ) is a Java library that provides machine learning based toolkits for NLP tasks. Fu-danNLP (Qiu et al., 2013 ) is a Java based library which integrates the machine learning models and datasets for Chinese NLP.",
"cite_spans": [
{
"start": 7,
"end": 26,
"text": "(Afgan et al., 2018",
"ref_id": "BIBREF0"
},
{
"start": 393,
"end": 417,
"text": "(El-Assady et al., 2019)",
"ref_id": "BIBREF6"
},
{
"start": 498,
"end": 516,
"text": "(Rak et al., 2012)",
"ref_id": "BIBREF19"
},
{
"start": 688,
"end": 711,
"text": "(Hardeniya et al., 2016",
"ref_id": "BIBREF7"
},
{
"start": 799,
"end": 819,
"text": "(Morton et al., 2005",
"ref_id": "BIBREF17"
},
{
"start": 911,
"end": 928,
"text": "(Qiu et al., 2013",
"ref_id": "BIBREF18"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "In the medical domain, NILE (Yu and Cai, 2013 ) is a Java package which includes rule based NLP methods for information extraction from medical notes. Apache cTAKES (Apache cTAKES, 2018) and CLAMP (Soysal et al., 2018) are two clinical NLP systems with pipeline-based architecture in the UIMA framework. Both systems have a graphical user interface, allowing users to build pipelines from build-in UIMA components. However, the UIMA framework has a steep learning curve. It is also not widely used in the machine-learning-based NLP research. Furthermore, most NLP applications are often released as command line programs. Therefore, it is hard to extend applications that use the UIMA framework with new models. In contrast, tools on our BENTO platform are based on command line programs and users can easily define their own tools with little restriction.",
"cite_spans": [
{
"start": 28,
"end": 45,
"text": "(Yu and Cai, 2013",
"ref_id": "BIBREF24"
},
{
"start": 197,
"end": 218,
"text": "(Soysal et al., 2018)",
"ref_id": "BIBREF22"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "BENTO mainly comprises three parts: a front-end web application, a BENTO back end server and a CodaLab back end. As shown in Figure 1 , BENTO has a web-based user interface, from which users can upload data, edit tools, submit jobs and perform various other operations. The BENTO back end is a web server that is mainly used for storing the tools, including the user-defined ones, so they can be accessed in different sessions. The CodaLab back end is used for execution of each computational job. When a tool is being executed, BENTO will generate a series of CodaLab commands based on the tool information and the input bundles. The outputs of the tool are the run bundles generated from those commands which can be passed on to the down-stream tools and inspected by the users on the CodaLab interface.",
"cite_spans": [],
"ref_spans": [
{
"start": 125,
"end": 133,
"text": "Figure 1",
"ref_id": null
}
],
"eq_spans": [],
"section": "System Description",
"sec_num": "3"
},
{
"text": "As shown in Figure 2 , the user interface of our platform is a web application that can be roughly divided into three panels from left to right: tool panel, canvas panel and worksheet panel. The tool panel lists the current available tools on the platform organized in a hierarchical file system struc- ture along with the meta information. Users can edit the User Tools folder using the buttons listed on the top menu bar. To run a tool, users can simply drag it to the canvas panel to the right and a tool node will appear on the canvas. A node, shown in the workflow Figure 2 , contains several input and output ports, corresponding to the inputs and outputs of the tool.",
"cite_spans": [],
"ref_spans": [
{
"start": 12,
"end": 20,
"text": "Figure 2",
"ref_id": "FIGREF0"
},
{
"start": 570,
"end": 578,
"text": "Figure 2",
"ref_id": "FIGREF0"
}
],
"eq_spans": [],
"section": "Web Interface",
"sec_num": "3.1"
},
{
"text": "Tool nodes can be linked together to form a pipeline and the connections represent the data flow during execution (Figure 2) . A connection starts from an output port and ends in an input port. An input port accepts only a single connection while an output port can initiate one or multiple connections. Users can edit the tool by clicking the Editor button ( ) on the top right corner and the node will be toggled to an editor interface (Figure 3) . The editor contains the expression of the tool (Section 3.3), which can be modified by the users. The rightmost part is the worksheet panel that displays the content of the current selected worksheet. Worksheets are editable markup documents provided by CodaLab. Dragging a bundle entry from the worksheet panel to the canvas will create a data node. A data node is similar to the tool node except that it does not have any input port which naturally represents a data entity in a computational pipeline.",
"cite_spans": [],
"ref_spans": [
{
"start": 114,
"end": 124,
"text": "(Figure 2)",
"ref_id": "FIGREF0"
},
{
"start": 438,
"end": 448,
"text": "(Figure 3)",
"ref_id": "FIGREF1"
}
],
"eq_spans": [],
"section": "Web Interface",
"sec_num": "3.1"
},
{
"text": "3 For simplicity, pre-processing steps like tokenization is built-in in each tool.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Web Interface",
"sec_num": "3.1"
},
{
"text": "An important design goal of BENTO is flexibility. Users should be able to easily define their own tools on the BENTO platform and customize existing tools at the command line level. For this reason, we use CodaLab as the back end for tool execution on the BENTO platform. CodaLab is a cloud-based platform designed for running computational experiments in data-oriented research. In CodaLab, researchers can easily set up a reproducible environment and run arbitrary command line by specifying a docker image and bundle dependencies. In Co-daLab, bundles are immutable objects that hold the content of datasets. The output files produced by that command will be saved into a new bundle and can be further passed to down-stream experiments.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "CodaLab Back End",
"sec_num": "3.2"
},
{
"text": "All datasets in BENTO are stored as CodaLab bundles. The tools and pipelines will be compiled into CodaLab commands. Users could submit commands to the CodaLab back end via the web interface. Such design makes the computational results of the BENTO platform reproducible through Co-daLab. Since CodaLab will record dependency information in run bundles, it is also easy to recreate the pipeline on our platform from existing result bundles. Using CodaLab as the back end also mitigates the engineering challenges such as job scheduling and data management. Figure 2 . The expression can be roughly split into three sections indicated by the dashed squares. The first section declares the arguments of this tool. As seen, the tool takes three bundles as inputs: config, input and pretrained model. The second section declares a constant code which is initialized with an existing bundle. The third section is a string template for generating the CodaLab command.",
"cite_spans": [],
"ref_spans": [
{
"start": 557,
"end": 565,
"text": "Figure 2",
"ref_id": "FIGREF0"
}
],
"eq_spans": [],
"section": "CodaLab Back End",
"sec_num": "3.2"
},
{
"text": "The BENTO back end is for storing tools and generating CodaLab commands from the pipeline graphs.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "BENTO Back End",
"sec_num": "3.3"
},
{
"text": "The tools in BENTO are described via our custom language called CodaLang 4 It acts as an intermediate layer between the web interface and CodaLab. It has a succinct syntax for specifying the interfaces of a tool, i.e. the inputs and outputs. It also provides a string template mechanism for creating CodaLab commands from input arguments. For example, the CodaLang expression for the node NER in Figure 2 is shown in Figure 3 . The configuration is composed of three sections which are highlighted with dotted squares. The first section declares the arguments of the tool, corresponding to the three input ports of the node. The second section creates a constant variable code which is assigned an existing bundle. The third section is a string template for generating the Co-4 A thorough introduction can be found at https://github.com/jyh1/codalang . Figure 4 : The CodaLab commands generated from the pipeline in Figure 2 . Two CodaLab commands are generated based on two steps in the pipeline, namely NER and relation extraction. The bundle dependency information is highlighted in orange and the shell commands are colorized in red. The results in the first step are saved in the variable bundle 0 (circled in blue squares), which is used as a bundle dependency in the command of the second step. daLab command. It includes execution options (e.g., request-docker-image) and tool bash commands. The template variables are circled by the squares in the same color with their declarations.",
"cite_spans": [],
"ref_spans": [
{
"start": 396,
"end": 404,
"text": "Figure 2",
"ref_id": "FIGREF0"
},
{
"start": 417,
"end": 425,
"text": "Figure 3",
"ref_id": "FIGREF1"
},
{
"start": 853,
"end": 861,
"text": "Figure 4",
"ref_id": null
},
{
"start": 916,
"end": 924,
"text": "Figure 2",
"ref_id": "FIGREF0"
}
],
"eq_spans": [],
"section": "CodaLang: A Tool Configuration Language",
"sec_num": "3.3.1"
},
{
"text": "Once the values of the tool arguments are determined, a CodaLab command can be easily generated based on the command template. The run bundle created by the command will be used as results and can be passed on to down-stream tools in the pipeline. Through CodaLang, users can easily modify existing tools or create their own tools. The tool configuration can also be automatically generated from the dependency information of a bundle.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "CodaLang: A Tool Configuration Language",
"sec_num": "3.3.1"
},
{
"text": "We have described how BENTO transforms a single tool to a CodaLab command. In this section, we will describe how BENTO transforms a tool pipeline into multiple CodaLab commands. In a tool pipeline, tools are connected together to form a directed acyclic graph. During execution, tools are transformed to CodaLab commands according to their topological order in the graph. Take the pipeline in Figure 2 as an example, its corresponding CodaLab commands shown in Figure 4 .",
"cite_spans": [],
"ref_spans": [
{
"start": 393,
"end": 401,
"text": "Figure 2",
"ref_id": "FIGREF0"
},
{
"start": 461,
"end": 469,
"text": "Figure 4",
"ref_id": null
}
],
"eq_spans": [],
"section": "Pipeline Execution",
"sec_num": "3.3.2"
},
{
"text": "As shown in Figure 4 , the bundle dependency information is highlighted in orange and the shell commands are colorized in red. The two CodaLab commands correspond to the two tool nodes in the pipeline of Figure 2 . The first command is generated from the tool NER based on its tool configuration in Figure 3 . The results of this command are saved in the variable bundle 0, which will be em-ployed as a bundle dependency in the command of the tool for relation extraction. The web interface takes the responsibility of submitting the generated commands to CodaLab. When the pipeline begins to run, the worksheet panel will display the information of the newly created run bundles.",
"cite_spans": [],
"ref_spans": [
{
"start": 12,
"end": 20,
"text": "Figure 4",
"ref_id": null
},
{
"start": 204,
"end": 212,
"text": "Figure 2",
"ref_id": "FIGREF0"
},
{
"start": 299,
"end": 307,
"text": "Figure 3",
"ref_id": "FIGREF1"
}
],
"eq_spans": [],
"section": "Pipeline Execution",
"sec_num": "3.3.2"
},
{
"text": "In this section, we list the tools that have already been integrated to our platform, including:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Tools Integrated in BENTO",
"sec_num": "4"
},
{
"text": "\u2022 Hypoglycemic Event Detection : Hypoglycemic events are common and potentially dangerous conditions among patients being treated for diabetes. This tool can be used to automatically detect hypoglycemic events from EHR notes.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Tools Integrated in BENTO",
"sec_num": "4"
},
{
"text": "\u2022 Clinical Entity Recognition (Li et al., 2018) : This tool has been built to recognize 9 types of clinical entities such as medications, indications and adverse drug events (ADEs).",
"cite_spans": [
{
"start": 30,
"end": 47,
"text": "(Li et al., 2018)",
"ref_id": "BIBREF13"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Tools Integrated in BENTO",
"sec_num": "4"
},
{
"text": "\u2022 Clinical Relation Extraction (Li et al., 2018) : This tool is able to extract 7 types of relations between clinical entities such as medications and their durations, dosages and frequencies.",
"cite_spans": [
{
"start": 31,
"end": 48,
"text": "(Li et al., 2018)",
"ref_id": "BIBREF13"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Tools Integrated in BENTO",
"sec_num": "4"
},
{
"text": "\u2022 Disease Name Normalization : This tool can be used to normalize disease names to some controlled vocabularies such SNOMED 5 and MEDIC (Davis et al., 2012) .",
"cite_spans": [
{
"start": 136,
"end": 156,
"text": "(Davis et al., 2012)",
"ref_id": "BIBREF3"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Tools Integrated in BENTO",
"sec_num": "4"
},
{
"text": "\u2022 De-identification: This tool is able to recognize 18 types of protected health information that needs to be removed to de-identify patient notes. We employed BERT (Devlin et al., 2019) to build a de-identification model whose performance is comparable with the state-ofthe-art system (Dernoncourt et al., 2017) .",
"cite_spans": [
{
"start": 165,
"end": 186,
"text": "(Devlin et al., 2019)",
"ref_id": "BIBREF5"
},
{
"start": 286,
"end": 312,
"text": "(Dernoncourt et al., 2017)",
"ref_id": "BIBREF4"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Tools Integrated in BENTO",
"sec_num": "4"
},
{
"text": "We provide examples and instructions to use these tools on the demo page of our platform. For convenience, these tools all take plain text files as inputs and have the pre-processing and tokenization components built-in. In the future, we will integrate stand-alone components dedicated for preprocessing and tokenization to BENTO which can be shared by different application tools. We also plan to incorporate more NLP tools developed by our group (Rumeng et al., 2017; Lalor et al., 2019; Zheng and Yu, 2018) .",
"cite_spans": [
{
"start": 449,
"end": 470,
"text": "(Rumeng et al., 2017;",
"ref_id": "BIBREF21"
},
{
"start": 471,
"end": 490,
"text": "Lalor et al., 2019;",
"ref_id": "BIBREF11"
},
{
"start": 491,
"end": 510,
"text": "Zheng and Yu, 2018)",
"ref_id": "BIBREF25"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Tools Integrated in BENTO",
"sec_num": "4"
},
{
"text": "In this paper, we have described the design of the workflow management platform BENTO. To the best of our knowledge, BENTO represents the first web-based workflow management platform for NLP research. Using BENTO, researchers can make use of existing tools or define their own tools. Computational pipelines can be configured through a web-based user-interface and then automatically executed on CodaLab. BENTO includes a number of clinical NLP tools to facilitate the process of EHR notes. A demo of our platform is available at bio-nlp.org/bentodemo/.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion",
"sec_num": "5"
},
{
"text": "docker.com",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "https://www.snomed.org",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
}
],
"back_matter": [],
"bib_entries": {
"BIBREF0": {
"ref_id": "b0",
"title": "The galaxy platform for accessible, reproducible and collaborative biomedical analyses",
"authors": [
{
"first": "Enis",
"middle": [],
"last": "Afgan",
"suffix": ""
},
{
"first": "Dannon",
"middle": [],
"last": "Baker",
"suffix": ""
},
{
"first": "B\u00e9r\u00e9nice",
"middle": [],
"last": "Batut",
"suffix": ""
},
{
"first": "Marius",
"middle": [],
"last": "Van Den",
"suffix": ""
},
{
"first": "Dave",
"middle": [],
"last": "Beek",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Bouvier",
"suffix": ""
},
{
"first": "John",
"middle": [],
"last": "Martin\u010dech",
"suffix": ""
},
{
"first": "Dave",
"middle": [],
"last": "Chilton",
"suffix": ""
},
{
"first": "Nate",
"middle": [],
"last": "Clements",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Coraor",
"suffix": ""
},
{
"first": "A",
"middle": [],
"last": "Bj\u00f6rn",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Gr\u00fcning",
"suffix": ""
}
],
"year": 2018,
"venue": "update. Nucleic acids research",
"volume": "46",
"issue": "W1",
"pages": "537--544",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Enis Afgan, Dannon Baker, B\u00e9r\u00e9nice Batut, Marius Van Den Beek, Dave Bouvier, Martin\u010cech, John Chilton, Dave Clements, Nate Coraor, Bj\u00f6rn A Gr\u00fcning, et al. 2018. The galaxy platform for ac- cessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic acids research, 46(W1):W537-W544.",
"links": null
},
"BIBREF1": {
"ref_id": "b1",
"title": "clinical text analysis knowledge extraction system",
"authors": [
{
"first": "",
"middle": [],
"last": "Tm Apache Ctakes",
"suffix": ""
}
],
"year": 2018,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "TM Apache cTAKES. 2018. clinical text analysis knowledge extraction system.",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "A method for named entity normalization in biomedical articles: application to diseases and plants",
"authors": [
{
"first": "Hyejin",
"middle": [],
"last": "Cho",
"suffix": ""
},
{
"first": "Wonjun",
"middle": [],
"last": "Choi",
"suffix": ""
},
{
"first": "Hyunju",
"middle": [],
"last": "Lee",
"suffix": ""
}
],
"year": 2017,
"venue": "BMC bioinformatics",
"volume": "18",
"issue": "1",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Hyejin Cho, Wonjun Choi, and Hyunju Lee. 2017. A method for named entity normalization in biomedi- cal articles: application to diseases and plants. BMC bioinformatics, 18(1):451.",
"links": null
},
"BIBREF3": {
"ref_id": "b3",
"title": "Medic: a practical disease vocabulary used at the comparative toxicogenomics database",
"authors": [
{
"first": "Allan",
"middle": [
"Peter"
],
"last": "Davis",
"suffix": ""
},
{
"first": "C",
"middle": [],
"last": "Thomas",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Wiegers",
"suffix": ""
},
{
"first": "Carolyn",
"middle": [
"J"
],
"last": "Michael C Rosenstein",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Mattingly",
"suffix": ""
}
],
"year": 2012,
"venue": "Database",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Allan Peter Davis, Thomas C Wiegers, Michael C Rosenstein, and Carolyn J Mattingly. 2012. Medic: a practical disease vocabulary used at the compara- tive toxicogenomics database. Database, 2012.",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "De-identification of patient notes with recurrent neural networks",
"authors": [
{
"first": "Franck",
"middle": [],
"last": "Dernoncourt",
"suffix": ""
},
{
"first": "Ji",
"middle": [
"Young"
],
"last": "Lee",
"suffix": ""
},
{
"first": "Ozlem",
"middle": [],
"last": "Uzuner",
"suffix": ""
},
{
"first": "Peter",
"middle": [],
"last": "Szolovits",
"suffix": ""
}
],
"year": 2017,
"venue": "Journal of the American Medical Informatics Association",
"volume": "24",
"issue": "3",
"pages": "596--606",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Franck Dernoncourt, Ji Young Lee, Ozlem Uzuner, and Peter Szolovits. 2017. De-identification of pa- tient notes with recurrent neural networks. Journal of the American Medical Informatics Association, 24(3):596-606.",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "BERT: Pre-training of deep bidirectional transformers for language understanding",
"authors": [
{
"first": "Jacob",
"middle": [],
"last": "Devlin",
"suffix": ""
},
{
"first": "Ming-Wei",
"middle": [],
"last": "Chang",
"suffix": ""
},
{
"first": "Kenton",
"middle": [],
"last": "Lee",
"suffix": ""
},
{
"first": "Kristina",
"middle": [],
"last": "Toutanova",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
"volume": "1",
"issue": "",
"pages": "4171--4186",
"other_ids": {
"DOI": [
"10.18653/v1/N19-1423"
]
},
"num": null,
"urls": [],
"raw_text": "Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language under- standing. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171-4186, Minneapolis, Minnesota. Associ- ation for Computational Linguistics.",
"links": null
},
"BIBREF6": {
"ref_id": "b6",
"title": "lingvis. io-a linguistic visual analytics framework",
"authors": [
{
"first": "Mennatallah",
"middle": [],
"last": "El-Assady",
"suffix": ""
},
{
"first": "Wolfgang",
"middle": [],
"last": "Jentner",
"suffix": ""
},
{
"first": "Fabian",
"middle": [],
"last": "Sperrle",
"suffix": ""
},
{
"first": "Rita",
"middle": [],
"last": "Sevastjanova",
"suffix": ""
},
{
"first": "Annette",
"middle": [],
"last": "Hautli",
"suffix": ""
},
{
"first": "Miriam",
"middle": [],
"last": "Butt",
"suffix": ""
},
{
"first": "Daniel",
"middle": [],
"last": "Keim",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
"volume": "",
"issue": "",
"pages": "13--18",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Mennatallah El-Assady, Wolfgang Jentner, Fabian Sperrle, Rita Sevastjanova, Annette Hautli, Miriam Butt, and Daniel Keim. 2019. lingvis. io-a linguistic visual analytics framework. In Proceedings of the 57th Annual Meeting of the Association for Compu- tational Linguistics: System Demonstrations, pages 13-18.",
"links": null
},
"BIBREF7": {
"ref_id": "b7",
"title": "Natural Language Processing: Python and NLTK",
"authors": [
{
"first": "Nitin",
"middle": [],
"last": "Hardeniya",
"suffix": ""
},
{
"first": "Jacob",
"middle": [],
"last": "Perkins",
"suffix": ""
},
{
"first": "Deepti",
"middle": [],
"last": "Chopra",
"suffix": ""
},
{
"first": "Nisheeth",
"middle": [],
"last": "Joshi",
"suffix": ""
},
{
"first": "Iti",
"middle": [],
"last": "Mathur",
"suffix": ""
}
],
"year": 2016,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Nitin Hardeniya, Jacob Perkins, Deepti Chopra, Nisheeth Joshi, and Iti Mathur. 2016. Natural Lan- guage Processing: Python and NLTK. Packt Pub- lishing Ltd.",
"links": null
},
"BIBREF8": {
"ref_id": "b8",
"title": "Classifying medical relations in clinical text via convolutional neural networks",
"authors": [
{
"first": "Bin",
"middle": [],
"last": "He",
"suffix": ""
},
{
"first": "Yi",
"middle": [],
"last": "Guan",
"suffix": ""
},
{
"first": "Rui",
"middle": [],
"last": "Dai",
"suffix": ""
}
],
"year": 2019,
"venue": "Artificial intelligence in medicine",
"volume": "93",
"issue": "",
"pages": "43--49",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Bin He, Yi Guan, and Rui Dai. 2019. Classifying med- ical relations in clinical text via convolutional neural networks. Artificial intelligence in medicine, 93:43- 49.",
"links": null
},
"BIBREF9": {
"ref_id": "b9",
"title": "Structured prediction models for rnn based sequence labeling in clinical text",
"authors": [
{
"first": "N",
"middle": [],
"last": "Abhyuday",
"suffix": ""
},
{
"first": "Hong",
"middle": [],
"last": "Jagannatha",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Yu",
"suffix": ""
}
],
"year": 2016,
"venue": "Proceedings of the conference on empirical methods in natural language processing. conference on empirical methods in natural language processing",
"volume": "2016",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Abhyuday N Jagannatha and Hong Yu. 2016. Struc- tured prediction models for rnn based sequence la- beling in clinical text. In Proceedings of the confer- ence on empirical methods in natural language pro- cessing. conference on empirical methods in natural language processing, volume 2016, page 856. NIH Public Access.",
"links": null
},
"BIBREF10": {
"ref_id": "b10",
"title": "Automatic Detection of Hypoglycemic Events From the Electronic Health Record Notes of Diabetes Patients: Empirical Study",
"authors": [
{
"first": "Yonghao",
"middle": [],
"last": "Jin",
"suffix": ""
},
{
"first": "Fei",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "G",
"middle": [],
"last": "Varsha",
"suffix": ""
},
{
"first": "Hong",
"middle": [],
"last": "Vimalananda",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Yu",
"suffix": ""
}
],
"year": 2019,
"venue": "JMIR medical informatics",
"volume": "7",
"issue": "4",
"pages": "",
"other_ids": {
"DOI": [
"10.2196/14340"
]
},
"num": null,
"urls": [],
"raw_text": "Yonghao Jin, Fei Li, Varsha G Vimalananda, and Hong Yu. 2019. Automatic Detection of Hypoglycemic Events From the Electronic Health Record Notes of Diabetes Patients: Empirical Study. JMIR medical informatics, 7(4):e14340.",
"links": null
},
"BIBREF11": {
"ref_id": "b11",
"title": "Improving electronic health record note comprehension with noteaid: Randomized trial of electronic health record note comprehension interventions with crowdsourced workers",
"authors": [
{
"first": "P",
"middle": [],
"last": "John",
"suffix": ""
},
{
"first": "Beverly",
"middle": [],
"last": "Lalor",
"suffix": ""
},
{
"first": "Hong",
"middle": [],
"last": "Woolf",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Yu",
"suffix": ""
}
],
"year": 2019,
"venue": "Journal of medical Internet research",
"volume": "21",
"issue": "1",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "John P Lalor, Beverly Woolf, and Hong Yu. 2019. Im- proving electronic health record note comprehen- sion with noteaid: Randomized trial of electronic health record note comprehension interventions with crowdsourced workers. Journal of medical Internet research, 21(1):e10793.",
"links": null
},
"BIBREF12": {
"ref_id": "b12",
"title": "Fine-Tuning Bidirectional Encoder Representations From Transformers (BERT)-Based Models on Large-Scale Electronic Health Record Notes: An Empirical Study",
"authors": [
{
"first": "Fei",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Yonghao",
"middle": [],
"last": "Jin",
"suffix": ""
},
{
"first": "Weisong",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "Bhanu Pratap Singh",
"middle": [],
"last": "Rawat",
"suffix": ""
},
{
"first": "Pengshan",
"middle": [],
"last": "Cai",
"suffix": ""
},
{
"first": "Hong",
"middle": [],
"last": "Yu",
"suffix": ""
}
],
"year": 2019,
"venue": "JMIR medical informatics",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"DOI": [
"10.2196/14830"
]
},
"num": null,
"urls": [],
"raw_text": "Fei Li, Yonghao Jin, Weisong Liu, Bhanu Pratap Singh Rawat, Pengshan Cai, and Hong Yu. 2019. Fine- Tuning Bidirectional Encoder Representations From Transformers (BERT)-Based Models on Large- Scale Electronic Health Record Notes: An Empiri- cal Study. JMIR medical informatics.",
"links": null
},
"BIBREF13": {
"ref_id": "b13",
"title": "Extraction of Information Related to Adverse Drug Events from Electronic Health Record Notes: Design of an Endto-End Model Based on Deep Learning",
"authors": [
{
"first": "Fei",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Weisong",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "Hong",
"middle": [],
"last": "Yu",
"suffix": ""
}
],
"year": 2018,
"venue": "JMIR medical informatics",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"DOI": [
"10.2196/12159"
]
},
"num": null,
"urls": [],
"raw_text": "Fei Li, Weisong Liu, and Hong Yu. 2018. Extraction of Information Related to Adverse Drug Events from Electronic Health Record Notes: Design of an End- to-End Model Based on Deep Learning. JMIR med- ical informatics.",
"links": null
},
"BIBREF14": {
"ref_id": "b14",
"title": "Cnn-based ranking for biomedical entity normalization",
"authors": [
{
"first": "Haodi",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Qingcai",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Buzhou",
"middle": [],
"last": "Tang",
"suffix": ""
},
{
"first": "Xiaolong",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "Hua",
"middle": [],
"last": "Xu",
"suffix": ""
},
{
"first": "Baohua",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "Dong",
"middle": [],
"last": "Huang",
"suffix": ""
}
],
"year": 2017,
"venue": "BMC bioinformatics",
"volume": "18",
"issue": "11",
"pages": "79--86",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Haodi Li, Qingcai Chen, Buzhou Tang, Xiaolong Wang, Hua Xu, Baohua Wang, and Dong Huang. 2017. Cnn-based ranking for biomedical entity nor- malization. BMC bioinformatics, 18(11):79-86.",
"links": null
},
"BIBREF15": {
"ref_id": "b15",
"title": "De-identification of clinical notes via recurrent neural network and conditional random field",
"authors": [
{
"first": "Zengjian",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "Buzhou",
"middle": [],
"last": "Tang",
"suffix": ""
},
{
"first": "Xiaolong",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "Qingcai",
"middle": [],
"last": "Chen",
"suffix": ""
}
],
"year": 2017,
"venue": "Journal of biomedical informatics",
"volume": "75",
"issue": "",
"pages": "34--42",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Zengjian Liu, Buzhou Tang, Xiaolong Wang, and Qing- cai Chen. 2017. De-identification of clinical notes via recurrent neural network and conditional random field. Journal of biomedical informatics, 75:S34- S42.",
"links": null
},
"BIBREF16": {
"ref_id": "b16",
"title": "The stanford corenlp natural language processing toolkit",
"authors": [
{
"first": "Christopher",
"middle": [],
"last": "Manning",
"suffix": ""
},
{
"first": "Mihai",
"middle": [],
"last": "Surdeanu",
"suffix": ""
},
{
"first": "John",
"middle": [],
"last": "Bauer",
"suffix": ""
},
{
"first": "Jenny",
"middle": [],
"last": "Finkel",
"suffix": ""
},
{
"first": "Steven",
"middle": [],
"last": "Bethard",
"suffix": ""
},
{
"first": "David",
"middle": [],
"last": "Mcclosky",
"suffix": ""
}
],
"year": 2014,
"venue": "Proceedings of 52nd annual meeting of the association for computational linguistics: system demonstrations",
"volume": "",
"issue": "",
"pages": "55--60",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Christopher Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven Bethard, and David McClosky. 2014. The stanford corenlp natural language pro- cessing toolkit. In Proceedings of 52nd annual meet- ing of the association for computational linguistics: system demonstrations, pages 55-60.",
"links": null
},
"BIBREF17": {
"ref_id": "b17",
"title": "Opennlp: A java-based nlp toolkit",
"authors": [
{
"first": "Thomas",
"middle": [],
"last": "Morton",
"suffix": ""
},
{
"first": "Joern",
"middle": [],
"last": "Kottmann",
"suffix": ""
},
{
"first": "Jason",
"middle": [],
"last": "Baldridge",
"suffix": ""
},
{
"first": "Gann",
"middle": [],
"last": "Bierner",
"suffix": ""
}
],
"year": 2005,
"venue": "Proc. EACL",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Thomas Morton, Joern Kottmann, Jason Baldridge, and Gann Bierner. 2005. Opennlp: A java-based nlp toolkit. In Proc. EACL.",
"links": null
},
"BIBREF18": {
"ref_id": "b18",
"title": "Fudannlp: A toolkit for chinese natural language processing",
"authors": [
{
"first": "Xipeng",
"middle": [],
"last": "Qiu",
"suffix": ""
},
{
"first": "Qi",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Xuan-Jing",
"middle": [],
"last": "Huang",
"suffix": ""
}
],
"year": 2013,
"venue": "Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
"volume": "",
"issue": "",
"pages": "49--54",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Xipeng Qiu, Qi Zhang, and Xuan-Jing Huang. 2013. Fudannlp: A toolkit for chinese natural language processing. In Proceedings of the 51st Annual Meet- ing of the Association for Computational Linguistics: System Demonstrations, pages 49-54.",
"links": null
},
"BIBREF19": {
"ref_id": "b19",
"title": "Argo: an integrative, interactive, text mining-based workbench supporting curation",
"authors": [
{
"first": "Rafal",
"middle": [],
"last": "Rak",
"suffix": ""
},
{
"first": "Andrew",
"middle": [],
"last": "Rowley",
"suffix": ""
},
{
"first": "William",
"middle": [],
"last": "Black",
"suffix": ""
},
{
"first": "Sophia",
"middle": [],
"last": "Ananiadou",
"suffix": ""
}
],
"year": 2012,
"venue": "Database",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Rafal Rak, Andrew Rowley, William Black, and Sophia Ananiadou. 2012. Argo: an integrative, interactive, text mining-based workbench supporting curation. Database, 2012.",
"links": null
},
"BIBREF20": {
"ref_id": "b20",
"title": "Naranjo question answering using end-to-end multitask learning model",
"authors": [
{
"first": "Fei",
"middle": [],
"last": "Bhanu Pratap Singh Rawat",
"suffix": ""
},
{
"first": "Hong",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Yu",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining",
"volume": "",
"issue": "",
"pages": "2547--2555",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Bhanu Pratap Singh Rawat, Fei Li, and Hong Yu. 2019. Naranjo question answering using end-to-end multi- task learning model. In Proceedings of the 25th ACM SIGKDD International Conference on Knowl- edge Discovery & Data Mining, pages 2547-2555.",
"links": null
},
"BIBREF21": {
"ref_id": "b21",
"title": "A hybrid neural network model for joint prediction of presence and period assertions of medical events in clinical notes",
"authors": [
{
"first": "Li",
"middle": [],
"last": "Rumeng",
"suffix": ""
},
{
"first": "Jagannatha",
"middle": [],
"last": "Abhyuday",
"suffix": ""
},
{
"first": "Yu",
"middle": [],
"last": "Hong",
"suffix": ""
}
],
"year": 2017,
"venue": "AMIA Annual Symposium Proceedings",
"volume": "2017",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Li Rumeng, N Jagannatha Abhyuday, and Yu Hong. 2017. A hybrid neural network model for joint pre- diction of presence and period assertions of medi- cal events in clinical notes. In AMIA Annual Sympo- sium Proceedings, volume 2017, page 1149. Ameri- can Medical Informatics Association.",
"links": null
},
"BIBREF22": {
"ref_id": "b22",
"title": "Clamp-a toolkit for efficiently building customized clinical natural language processing pipelines",
"authors": [
{
"first": "Ergin",
"middle": [],
"last": "Soysal",
"suffix": ""
},
{
"first": "Jingqi",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "Min",
"middle": [],
"last": "Jiang",
"suffix": ""
},
{
"first": "Yonghui",
"middle": [],
"last": "Wu",
"suffix": ""
},
{
"first": "Serguei",
"middle": [],
"last": "Pakhomov",
"suffix": ""
},
{
"first": "Hongfang",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "Hua",
"middle": [],
"last": "Xu",
"suffix": ""
}
],
"year": 2018,
"venue": "Journal of the American Medical Informatics Association",
"volume": "25",
"issue": "3",
"pages": "331--336",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ergin Soysal, Jingqi Wang, Min Jiang, Yonghui Wu, Serguei Pakhomov, Hongfang Liu, and Hua Xu. 2018. Clamp-a toolkit for efficiently build- ing customized clinical natural language processing pipelines. Journal of the American Medical Infor- matics Association, 25(3):331-336.",
"links": null
},
"BIBREF23": {
"ref_id": "b23",
"title": "A bidirectional lstm and conditional random fields approach to medical named entity recognition",
"authors": [
{
"first": "Kai",
"middle": [],
"last": "Xu",
"suffix": ""
},
{
"first": "Zhanfan",
"middle": [],
"last": "Zhou",
"suffix": ""
},
{
"first": "Tianyong",
"middle": [],
"last": "Hao",
"suffix": ""
},
{
"first": "Wenyin",
"middle": [],
"last": "Liu",
"suffix": ""
}
],
"year": 2017,
"venue": "International Conference on Advanced Intelligent Systems and Informatics",
"volume": "",
"issue": "",
"pages": "355--365",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Kai Xu, Zhanfan Zhou, Tianyong Hao, and Wenyin Liu. 2017. A bidirectional lstm and conditional random fields approach to medical named entity recognition. In International Conference on Advanced Intelligent Systems and Informatics, pages 355-365. Springer.",
"links": null
},
"BIBREF24": {
"ref_id": "b24",
"title": "Nile: fast natural language processing for electronic health records",
"authors": [
{
"first": "S",
"middle": [],
"last": "Yu",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Cai",
"suffix": ""
}
],
"year": 2013,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "S Yu and T Cai. 2013. Nile: fast natural language processing for electronic health records. Preprint at https://arxiv. org/abs/1311.6063.",
"links": null
},
"BIBREF25": {
"ref_id": "b25",
"title": "Assessing the readability of medical documents: a ranking approach",
"authors": [
{
"first": "Jiaping",
"middle": [],
"last": "Zheng",
"suffix": ""
},
{
"first": "Hong",
"middle": [],
"last": "Yu",
"suffix": ""
}
],
"year": 2018,
"venue": "JMIR medical informatics",
"volume": "6",
"issue": "1",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jiaping Zheng and Hong Yu. 2018. Assessing the read- ability of medical documents: a ranking approach. JMIR medical informatics, 6(1):e17.",
"links": null
}
},
"ref_entries": {
"FIGREF0": {
"text": "BENTO Web Interface. The interface can be roughly divided into three parts from left to right: tool panel, canvas panel and worksheet panel. The tool panel lists the current available tools organized in a tree view. The canvas panel contains the flowchart of the current pipeline. Every node represents a tool or dataset and each connection indicates the data flow in the pipeline. 3 This figure shows an example of the pipeline for entity and relation extraction. The worksheet panel displays the content of the CodaLab worksheet such as bundles and their UUIDs.",
"uris": null,
"type_str": "figure",
"num": null
},
"FIGREF1": {
"text": "The CodaLang expression for the tool NER in",
"uris": null,
"type_str": "figure",
"num": null
}
}
}
} |